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Friction-Robust Autonomous Racing Using Trajectory Optimization Over Multiple Models
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AI
DOI：10.1109/OJCSYS.2025.3635449.png)
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Abstract
En 中文
Autonomous vehicle control in low-friction environments should be capable of using all of the available traction at the road to accomplish maneuvering objectives. In these environments, however, the limit of traction is difficult to estimate, which challenges standard motion planning techniques. In this paper, we introduce a trajectory optimization framework that natively incorporates the complex and nonlinear effects of friction uncertainty into the planning process to improve both the performance and robustness of maneuvering at high accelerations. The core approach of the method is to explicitly consider a range of possible dynamics models, inclusive of their closed-loop behavior, simultaneously in the optimization. We illustrate this method through a racing example, where the minimum-time objective facilitates intuitive performance and robustness metrics (lap time and tracking error limits), all while necessitating vehicle maneuvering through nonlinear and friction-sensitive regions of the state space. Experiments on an autonomous VW Golf GTI on a challenging winter ice track demonstrate the efficacy of this approach.
Keywords:
Uncertainty
Friction
Planning
Trajectory optimization
Vehicle dynamics
Tires
Roads
Vectors
Robustness
Adaptation models
Autonomous vehicles
extreme maneuvering
friction uncertainty
robust trajectory optimization
Journal
I
IF:
1.2
Papers:
13
Citations:
0